84 research outputs found
Weak Chaos in large conservative system -- Infinite-range coupled standard maps
We study, through a new perspective, a globally coupled map system that
essentially interpolates between simple discrete-time nonlinear dynamics and
certain long-range many-body Hamiltonian models. In particular, we exhibit
relevant similarities, namely (i) the existence of long-standing
quasistationary states (QSS), and (ii) the emergence of weak chaos in the
thermodynamic limit, between the present model and the Hamiltonian Mean Field
model, a strong candidate for a nonxtensive statistical mechanical approach.Comment: 6 pages, 2 figures. Corrected typos in equation 4. Changed caption in
Fig. 1. Corrected references 2 and 6. Acknowledgements adde
On the diffusive anomalies in a long-range Hamiltonian system
We scrutinize the anomalies in diffusion observed in an extended long-range
system of classical rotors, the HMF model. Under suitable preparation, the
system falls into long-lived quasi-stationary states presenting super-diffusion
of rotor phases. We investigate the diffusive motion of phases by monitoring
the evolution of their probability density function for large system sizes.
These densities are shown to be of the -Gaussian form, , with parameter increasing with time before
reaching a steady value . From this perspective, we also discuss
the relaxation to equilibrium and show that diffusive motion in
quasi-stationary trajectories strongly depends on system size.Comment: 5 pages, 5 figures. References added and correcte
Synchronization learning of coupled chaotic maps
We study the dynamics of an ensemble of globally coupled chaotic logistic
maps under the action of a learning algorithm aimed at driving the system from
incoherent collective evolution to a state of spontaneous full synchronization.
Numerical calculations reveal a sharp transition between regimes of
unsuccessful and successful learning as the algorithm stiffness grows. In the
regime of successful learning, an optimal value of the stiffness is found for
which the learning time is minimal
On statistical properties of traded volume in financial markets
In this article we study the dependence degree of the traded volume of the
Dow Jones 30 constituent equities by using a nonextensive generalised form of
the Kullback-Leibler information measure. Our results show a slow decay of the
dependence degree as a function of the lag. This feature is compatible with the
existence of non-linearities in this type time series. In addition, we
introduce a dynamical mechanism whose associated stationary probability density
function (PDF) presents a good agreement with the empirical results.Comment: 6 pages, 4 figures, 1 table. Based on the talk presented at "News,
Expectations and Trends in Statistical Physics, NEXT-SigmaPhi 3rd
International Conference. 13-18 August 2005, Kolymbari CRETE" Multi-fractal
analysis section remove
Evolving leraning rules and emergence of cooperation in spatial prisioner´s dilemma
In the evolutionary Prisoner’s dilemma (PD) game, agent splay with each other and update their strategies in every generation according to some microscopic dynamical rule. Inits spatial version,
agents do not play with every other but, instead, interactonly with their neighbours, thus mimicking the existing of a social orcontactnetwork that defines who interacts with whom. In this work, we
explore evolutionary, spatial PD systems consisting of two types of agents, each with a certain update (reproduction, learning) rule. We investigate two different scenarios: in the first case, update rules
remain fixed for theen tire evolution of the system; in the second case, agents update both strategy and
update rule in every generation. We show that in a well mixed population the evolutionary out come is
always full defection. We subsequently focus on two strategy competition with nearest neighbour interactions on the contact network and synchronised update of strategies. Our results show that, for an
important range of the parameter sof the game, the final state of the system is largely different from that a rising from the usual setup of a single, fixed dynamical rule. Furthermore, the results are also very
different if update rules are fixed or evolve with the strategies. In these respect, we have studied
representative update rules, finding that some of them may become extinct while others prevail. We
describe the new and rich variety of final out comes that arise from this coevolutionary dynamics. We
include examples of other neighbourhoods and asynchronous updating that confirm the robustness of our conclusions. Our results pave the way to an evolutionary rationale for modelling social interactions
through game theory with a preferred set of update rules.This work was supported by Ministerio de Educación y Ciencia (Spain) under Grant MOSAICO and by Comunidad de Madrid (Spain) under Grant SIMUMAT CM.Publicad
Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes.
In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES.
Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativ
Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes.
In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES.
Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativ
Teoría de Grafos para la Identificación de Nodos Maliciosos en la Red
Se explora el reconocimiento de las botnets en una red a partir de su representación como grafo, extrayendo características a sus nodos y poniendo a prueba algoritmos de agrupamiento. Se logra la separación del 88% de las botnets junto al -0,14% de los nodos benignos.XI Workshop Seguridad Informática (WSI)Red de Universidades con Carreras en Informátic
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